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Higher Measurement Frequency and Stop-losses for Trend Followers?

| | Posted in: Technical Trading

Motivation to avoid being “burned by the turn” tempts trend followers to increase measurement frequency and/or use stop-losses. Do these approaches help momentum players jump the turn? In their October 2013 paper entitled “The Significance of Trading Frequency and Stop Loss in Trend Following Strategies”, Farzine Hachemian, Sebastien Tavernier and Anne-Sophie Van Royen assess whether increasing measurement frequency from weekly to daily and imposing stop-loss rules enhance the performance of trend-following strategies based on simple moving averages (SMA). They consider a set of 117 timing strategies that go long (short) when a fast SMA is higher (lower) than a slow SMA, with SMAs measured either weekly or daily. For the weekly (daily) signals, the fast SMA measurement interval ranges from 4 to 52 weeks (20 to 260 days) in increments of 4 weeks (20 days). Slow SMA measurement intervals range from 8 to 64 weeks (40 to 320 days) with the same increments. To avoid whipsaws, they insert a buffer equal to the 13-week (65-day) standard deviation of the fast SMA. They apply these strategies to 39 rolling series of the most liquid futures covering all asset classes and most geographies. They apply a round-trip trading friction of $30 and assume zero return on any cash above the required margin. They then add two kinds of stop-losses to the strategies, reset every six months: (1) a loss of five times the standard deviation of weekly or daily returns; or, (2) a loss of 1% of portfolio value. After a stop loss, they re-enter a similar position when the trading strategy generates a new signal or price recovers its previous high watermark. Using futures return data as specified during January 2000 through December 2012, they find that:

  • On average, the weekly (daily) strategies generate a net annualized return of 5.0% (5.3%), with standard deviation 18.7% (18.7%).
  • For comparable strategies, daily signals do not significantly outperform weekly signals based on either raw or risk-adjusted net performance. In other words, incremental daily trading frictions tend to offset any benefits of faster detection of trend changes.
    • For both daily and weekly measurement frequencies, strategies with shorter fast SMAs (20 and 40 days, or 4 and 8 weeks) generate the best risk-adjusted net returns. The best performing daily and weekly strategies employ slow/fast SMAs of 120/20 days and 24/4 weeks.
    • Performance metrics for specific strategies are highly variable over time. 
  • For both weekly and daily measurement frequencies, stop-loss rules are most effective during crashes, reducing maximum drawdowns by an average 17.2% and 16.5%, respectively. Stop-losses thus help avoid unrecoverable losses.
    • Stop-loss rules have little or no effect on average drawdown and time for recovery from drawdown.
    • Stop-losses are more effective for long-SMA strategies, which are naturally susceptible to crashes.
    • The best performing strategies benefit the least from stop-loss rules.

In summary, evidence indicates that increasing trend measurement frequency from weekly to daily offers no significant net advantage. Stop-loss rules reduce maximum drawdown but are a drag on the best-performing strategies.

Cautions regarding findings include:

  • The sample is short (13 years) for comparing annualized measurements.
  • The best (worst) combination of rules impounds data snooping bias, tending to overstate (understate) expected out-of-sample performance.
  • Significance tests assume tame return distributions. To the extent that futures series exhibit wild return distributions, these tests lose meaning.
  • The assumed level of trading frictions may not apply for some investors. In general, higher frictions hurt short-SMA strategies more than long-SMA strategies.

See “10-month Versus 40-week Versus 200-day SMA” and “Weekly or Monthly Asset Class ETF Momentum?” for conceptually similar measurement frequency tests. See “Do Stop Losses Work?” and “Using Trailing Stop Losses to Reduce Risk” for other research on stop-loss strategies.

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